Low-cost particulate matter sensors are transforming air quality monitoring because they have lower costs and greater mobility as compared to reference monitors. Calibration of these low-cost sensors requires training data from co-deployed reference monitors. Machine Learning based calibration gives better performance than conventional techniques, but requires a large amount of training data from the sensor, to be calibrated, co-deployed with a reference monitor. In this work, we propose novel transfer learning methods for quick calibration of sensors with minimal co-deployment with reference monitors. Transfer learning utilizes a large amount of data from other sensors along with a limited amount of data from the target sensor. Our extensive experimentation finds the proposed Model-Agnostic- Meta-Learning (MAML) based transfer learning method to be the most effective over other competitive baselines.
@article{arxiv.2108.00640,
title = {Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning},
author = {Kalpit Yadav and Vipul Arora and Sonu Kumar Jha and Mohit Kumar and Sachchida Nand Tripathi},
journal= {arXiv preprint arXiv:2108.00640},
year = {2021}
}
Comments
3+1 pages, submitted to IEEE sensors conference 2021